X-ray imageable, drug-loaded hydrogel that forms at body temperature for image-guided, needle- based locoregional drug delivery

Liver cancer ranks as the fifth leading cause of cancer-related death globally. Direct intratumoral injections of anti-cancer therapeutics may improve therapeutic efficacy and mitigate adverse effects compared to intravenous injections. Some challenges of intratumoral injections are that the liquid drug formulation may not remain localized and have unpredictable volumetric distribution. Thus, drug delivery varies widely, highly-dependent upon technique. An x-ray imageable poloxamer 407 (POL)-based drug delivery gel was developed and characterized, enabling real-time feedback. Utilizing three needle devices, POL or a control iodinated contrast solution were injected into an ex vivo bovine liver. The 3D distribution was assessed with cone beam computed tomography (CBCT). The 3D distribution of POL gels demonstrated localized spherical morphologies regardless of the injection rate. In addition, the gel 3D conformal distribution could be intentionally altered, depending on the injection technique. When doxorubicin (DOX) was loaded into the POL and injected, DOX distribution on optical imaging matched iodine distribution on CBCT suggesting spatial alignment of DOX and iodine localization in tissue. The controllability and localized deposition of this formulation may ultimately reduce the dependence on operator technique, reduce systemic side effects, and facilitate reproducibility across treatments, through more predictable standardized delivery.

Smartphone Augmented Reality Outperforms Conventional CT Guidance for Composite Ablation Margins in Phantom Models

PURPOSE

To develop and evaluate a smartphone augmented reality (AR) system for a large 50-mm liver tumor ablation with treatment planning for composite overlapping ablation zones.

MATERIALS AND METHODS

A smartphone AR application was developed to display tumor, probe, projected probe paths, ablated zones, and real-time percentage of the ablated target tumor volume. Fiducial markers were attached to phantoms and an ablation probe hub for tracking. The system was evaluated with tissue-mimicking thermochromic phantoms and gel phantoms. Four interventional radiologists performed 2 trials each of 3 probe insertions per trial using AR guidance versus computed tomography (CT) guidance approaches in 2 gel phantoms. Insertion points and optimal probe paths were predetermined. On Gel Phantom 2, serial ablated zones were saved and continuously displayed after each probe placement/adjustment, enabling feedback and iterative planning. The percentages of tumor ablated for AR guidance versus CT guidance, and with versus without display of recorded ablated zones, were compared among interventional radiologists with pairwise t-tests.

RESULTS

The means of percentages of tumor ablated for CT freehand and AR guidance were 36% ± 7 and 47% ± 4 (P = .004), respectively. The mean composite percentages of tumor ablated for AR guidance were 43% ± 1 (without) and 50% ± 2 (with display of ablation zone) (P = .033). There was no strong correlation between AR-guided percentage of ablation and years of experience (r < 0.5), whereas there was a strong correlation between CT-guided percentage of ablation and years of experience (r > 0.9).

CONCLUSIONS

A smartphone AR guidance system for dynamic iterative large liver tumor ablation was accurate, performed better than conventional CT guidance, especially for less experienced interventional radiologists, and enhanced more standardized performance across experience levels for ablation of a 50-mm tumor.

A Novel Magnetic Resonance Imaging/Ultrasound Fusion Prostate Biopsy Technique Using Transperineal Ultrasound: An Initial Experience

OBJECTIVE

To report an initial experience with a novel, "fully" transperineal (TP) prostate fusion biopsy using an unconstrained ultrasound transducer placed on the perineal skin to guide biopsy needles inserted via a TP approach.

METHODS

Conventional TP prostate biopsies for detection of prostate cancer have been performed with transrectal ultrasound, requiring specialized hardware, imposing limitations on needle trajectory, and contributing to patient discomfort. Seventy-six patients with known or suspected prostate cancer underwent 78 TP biopsy sessions in an academic center between June 2018 and April 2022 and were included in this study. These patients underwent TP prostate fusion biopsy using a grid or freehand device with transrectal ultrasound as well as TP prostate fusion biopsy using TP ultrasound in the same session. Per-session and per-lesion cancer detection rates were compared for conventional and fully TP biopsies using Fisher exact and McNemar's tests.

RESULTS

After a refinement period in 30 patients, 92 MRI-visible prostate lesions were sampled in 46 subsequent patients, along with repeat biopsies in 2 of the 30 patients from the refinement period. Grade group ≥2 cancer was diagnosed in 24/92 lesions (26%) on conventional TP biopsy (17 lesions with grid, 7 with freehand device), and in 25/92 lesions (27%) on fully TP biopsy (P = 1.00), with a 73/92 (79%) rate of agreement for grade group ≥2 cancer between the two methods.

CONCLUSION

Fully TP biopsy is feasible and may detect prostate cancer with detection rates comparable to conventional TP biopsy.

HoloLens augmented reality system for transperineal free-hand prostate procedures

Purpose

An augmented reality (AR) system was developed to facilitate free-hand real-time needle guidance for transperineal prostate (TP) procedures and to overcome the limitations of a traditional guidance grid.

Approach

The HoloLens AR system enables the superimposition of annotated anatomy derived from preprocedural volumetric images onto a patient and addresses the most challenging part of free-hand TP procedures by providing real-time needle tip localization and needle depth visualization during insertion. The AR system accuracy, or the image overlay accuracy ( n = 56 ), and needle targeting accuracy ( n = 24 ) were evaluated within a 3D-printed phantom. Three operators each used a planned-path guidance method ( n = 4 ) and free-hand guidance ( n = 4 ) to guide needles into targets in a gel phantom. Placement error was recorded. The feasibility of the system was further evaluated by delivering soft tissue markers into tumors of an anthropomorphic pelvic phantom via the perineum.

Results

The image overlay error was 1.29 ± 0.57    mm , and needle targeting error was 2.13 ± 0.52    mm . The planned-path guidance placements showed similar error compared to the free-hand guidance ( 4.14 ± 1.08    mm versus 4.20 ± 1.08    mm , p = 0.90 ). The markers were successfully implanted either into or in close proximity to the target lesion.

Conclusions

The HoloLens AR system can provide accurate needle guidance for TP interventions. AR support for free-hand lesion targeting is feasible and may provide more flexibility than grid-based methods, due to the real-time 3D and immersive experience during free-hand TP procedures.

Image-Guided Robotics for Standardized and Automated Biopsy and Ablation

Image-guided robotics for biopsy and ablation aims to minimize procedure times, reduce needle manipulations, radiation, and complications, and enable treatment of larger and more complex tumors, while facilitating standardization for more uniform and improved outcomes. Robotic navigation of needles enables standardized and uniform procedures which enhance reproducibility via real-time precision feedback, while avoiding radiation exposure to the operator. Robots can be integrated with computed tomography (CT), cone beam CT, magnetic resonance imaging, and ultrasound and through various techniques, including stereotaxy, table-mounted, floor-mounted, and patient-mounted robots. The history, challenges, solutions, and questions facing the field of interventional radiology (IR) and interventional oncology are reviewed, to enable responsible clinical adoption and value definition via ergonomics, workflows, business models, and outcome data. IR-integrated robotics is ready for broader adoption. The robots are coming!

Generalized chest CT and lab curves throughout the course of COVID-19

A better understanding of temporal relationships between chest CT and labs may provide a reference for disease severity over the disease course. Generalized curves of lung opacity volume and density over time can be used as standardized references from well before symptoms develop to over a month after recovery, when residual lung opacities remain. 739 patients with COVID-19 underwent CT and RT-PCR in an outbreak setting between January 21st and April 12th, 2020. 29 of 739 patients had serial exams (121 CTs and 279 laboratory measurements) over 50 ± 16 days, with an average of 4.2 sequential CTs each. Sequential volumes of total lung, overall opacity and opacity subtypes (ground glass opacity [GGO] and consolidation) were extracted using deep learning and manual segmentation. Generalized temporal curves of CT and laboratory measurements were correlated. Lung opacities appeared 3.4 ± 2.2 days prior to symptom onset. Opacity peaked 1 day after symptom onset. GGO onset was earlier and resolved later than consolidation. Lactate dehydrogenase, and C-reactive protein peaked earlier than procalcitonin and leukopenia. The temporal relationships of quantitative CT features and clinical labs have distinctive patterns and peaks in relation to symptom onset, which may inform early clinical course in patients with mild COVID-19 pneumonia, or may shed light upon chronic lung effects or mechanisms of medical countermeasures in clinical trials.

Comparison of Smartphone Augmented Reality, Smartglasses Augmented Reality, and 3D CBCT-guided Fluoroscopy Navigation for Percutaneous Needle Insertion: A Phantom Study

PURPOSE

To compare needle placement performance using an augmented reality (AR) navigation platform implemented on smartphone or smartglasses devices to that of CBCT-guided fluoroscopy in a phantom.

MATERIALS AND METHODS

An AR application was developed to display a planned percutaneous needle trajectory on the smartphone (iPhone7) and smartglasses (HoloLens1) devices in real time. Two AR-guided needle placement systems and CBCT-guided fluoroscopy with navigation software (XperGuide, Philips) were compared using an anthropomorphic phantom (CIRS, Norfolk, VA). Six interventional radiologists each performed 18 independent needle placements using smartphone (n = 6), smartglasses (n = 6), and XperGuide (n = 6) guidance. Placement error was defined as the distance from the needle tip to the target center. Placement time was recorded. For XperGuide, dose-area product (DAP, mGy*cm²) and fluoroscopy time (sec) were recorded. Statistical comparisons were made using a two-way repeated measures ANOVA.

RESULTS

The placement error using the smartphone, smartglasses, or XperGuide was similar (3.98 ± 1.68 mm, 5.18 ± 3.84 mm, 4.13 ± 2.38 mm, respectively, p = 0.11). Compared to CBCT-guided fluoroscopy, the smartphone and smartglasses reduced placement time by 38% (p = 0.02) and 55% (p = 0.001), respectively. The DAP for insertion using XperGuide was 3086 ± 2920 mGy*cm², and no intra-procedural radiation was required for augmented reality.

CONCLUSIONS

Smartphone- and smartglasses-based augmented reality reduced needle placement time and radiation exposure while maintaining placement accuracy compared to a clinically validated needle navigation platform.

Determination of disease severity in COVID-19 patients using deep learning in chest X-ray images

PURPOSE

Chest X-ray plays a key role in diagnosis and management of COVID-19 patients and imaging features associated with clinical elements may assist with the development or validation of automated image analysis tools. We aimed to identify associations between clinical and radiographic features as well as to assess the feasibility of deep learning applied to chest X-rays in the setting of an acute COVID-19 outbreak.

METHODS

A retrospective study of X-rays, clinical, and laboratory data was performed from 48 SARS-CoV-2 RT-PCR positive patients (age 60±17 years, 15 women) between February 22 and March 6, 2020 from a tertiary care hospital in Milan, Italy. Sixty-five chest X-rays were reviewed by two radiologists for alveolar and interstitial opacities and classified by severity on a scale from 0 to 3. Clinical factors (age, symptoms, comorbidities) were investigated for association with opacity severity and also with placement of central line or endotracheal tube. Deep learning models were then trained for two tasks: lung segmentation and opacity detection. Imaging characteristics were compared to clinical datapoints using the unpaired student's t-test or Mann-Whitney U test. Cohen's kappa analysis was used to evaluate the concordance of deep learning to conventional radiologist interpretation.

RESULTS

Fifty-six percent of patients presented with alveolar opacities, 73% had interstitial opacities, and 23% had normal X-rays. The presence of alveolar or interstitial opacities was statistically correlated with age (P = 0.008) and comorbidities (P = 0.005). The extent of alveolar or interstitial opacities on baseline X-ray was significantly associated with the presence of endotracheal tube (P = 0.0008 and P = 0.049) or central line (P = 0.003 and P = 0.007). In comparison to human interpretation, the deep learning model achieved a kappa concordance of 0.51 for alveolar opacities and 0.71 for interstitial opacities.

CONCLUSION

Chest X-ray analysis in an acute COVID-19 outbreak showed that the severity of opacities was associated with advanced age, comorbidities, as well as acuity of care. Artificial intelligence tools based upon deep learning of COVID-19 chest X-rays are feasible in the acute outbreak setting.

CT and clinical assessment in asymptomatic and pre-symptomatic patients with early SARS-CoV-2 in outbreak settings

Objectives

The early infection dynamics of patients with SARS-CoV-2 are not well understood. We aimed to investigate and characterize associations between clinical, laboratory, and imaging features of asymptomatic and pre-symptomatic patients with SARS-CoV-2.

Methods

Seventy-four patients with RT-PCR-proven SARS-CoV-2 infection were asymptomatic at presentation. All were retrospectively identified from 825 patients with chest CT scans and positive RT-PCR following exposure or travel risks in outbreak settings in Japan and China. CTs were obtained for every patient within a day of admission and were reviewed for infiltrate subtypes and percent with assistance from a deep learning tool. Correlations of clinical, laboratory, and imaging features were analyzed and comparisons were performed using univariate and multivariate logistic regression.

Results

Forty-eight of 74 (65%) initially asymptomatic patients had CT infiltrates that pre-dated symptom onset by 3.8 days. The most common CT infiltrates were ground glass opacities (45/48; 94%) and consolidation (22/48; 46%). Patient body temperature (p < 0.01), CRP (p < 0.01), and KL-6 (p = 0.02) were associated with the presence of CT infiltrates. Infiltrate volume (p = 0.01), percent lung involvement (p = 0.01), and consolidation (p = 0.043) were associated with subsequent development of symptoms.

Conclusions

COVID-19 CT infiltrates pre-dated symptoms in two-thirds of patients. Body temperature elevation and laboratory evaluations may identify asymptomatic patients with SARS-CoV-2 CT infiltrates at presentation, and the characteristics of CT infiltrates could help identify asymptomatic SARS-CoV-2 patients who subsequently develop symptoms. The role of chest CT in COVID-19 may be illuminated by a better understanding of CT infiltrates in patients with early disease or SARS-CoV-2 exposure.

Key Points

• Forty-eight of 74 (65%) pre-selected asymptomatic patients with SARS-CoV-2 had abnormal chest CT findings.

• CT infiltrates pre-dated symptom onset by 3.8 days (range 1–5).

• KL-6, CRP, and elevated body temperature identified patients with CT infiltrates. Higher infiltrate volume, percent lung involvement, and pulmonary consolidation identified patients who developed symptoms.

A Simple Flow Classification Parameter Can Discriminate Rupture Status in Intracranial Aneurysms

BACKGROUND

A simple dimensionless aneurysm number ($An$), which depends on geometry and flow pulsatility, was previously shown to distinguish the flow mode in intracranial aneurysms (IA): vortex mode with a dynamic vortex formation/evolution if $An > 1$, and cavity mode with a steady shear layer if $An < 1$.

OBJECTIVE

To hypothesize that $An\ > \ 1$ can distinguish rupture status because vortex mode is associated with high oscillatory shear index, which, in turn, is statistically associated with rupture.

METHODS

The above hypothesis is tested on a retrospective, consecutively collected database of 204 patient-specific IAs. The first 119 cases are assigned to training and the remainder to testing dataset. $An$ is calculated based on the pulsatility index (PI) approximated either from the literature or solving an optimization problem (denoted as$\ \widehat {PI}$). Student's t-test and logistic regression (LR) are used for hypothesis testing and data fitting, respectively.

RESULTS

$An$ can significantly discriminate ruptured and unruptured status with 95% confidence level (P < .0001). $An$ (using PI) and $\widehat {An}$ (using $\widehat {PI}$) significantly predict the ruptured IAs (for training dataset $An\!:\ $AUC = 0.85, $\widehat {An}\!:\ $AUC = 0.90, and for testing dataset $An\!:\ $sensitivity = 94%, specificity = 33%, $\widehat {An}\!:\ $sensitivity = 93.1%, specificity = 52.85%).

CONCLUSION

$An > 1$ predicts ruptured status. Unlike traditional hemodynamic parameters such as wall shear stress and oscillatory shear index, $An$ has a physical threshold of one (does not depend on statistical analysis) and does not require time-consuming flow simulations. Therefore, $An$ is a simple, practical discriminator of IA rupture status.

Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets

Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations.

Smartphone- versus smartglasses-based augmented reality (AR) for percutaneous needle interventions: system accuracy and feasibility study

PURPOSE

To compare the system accuracy and needle placement performance of smartphone- and smartglasses-based augmented reality (AR) for percutaneous needle interventions.

METHODS

An AR platform was developed to enable the superimposition of annotated anatomy and a planned needle trajectory onto a patient in real time. The system accuracy of the AR display on smartphone (iPhone7) and smartglasses (HoloLens1) devices was evaluated on a 3D-printed phantom. The target overlay error was measured as the distance between actual and virtual targets (n = 336) on the AR display, derived from preprocedural CT. The needle overlay angle was measured as the angular difference between actual and virtual needles (n = 12) on the AR display. Three operators each used the iPhone (n = 8), HoloLens (n = 8) and CT-guided freehand (n = 8) to guide needles into targets in a phantom. Needle placement error was measured with post-placement CT. Needle placement time was recorded from needle puncture to navigation completion.

RESULTS

The target overlay error of the iPhone was comparable to the HoloLens (1.75 ± 0.59 mm, 1.74 ± 0.86 mm, respectively, p = 0.9). The needle overlay angle of the iPhone and HoloLens was similar (0.28 ± 0.32°, 0.41 ± 0.23°, respectively, p = 0.26). The iPhone-guided needle placements showed reduced error compared to the HoloLens (2.58 ± 1.04 mm, 3.61 ± 2.25 mm, respectively, p = 0.05) and increased time (87 ± 17 s, 71 ± 27 s, respectively, p = 0.02). Both AR devices reduced placement error compared to CT-guided freehand (15.92 ± 8.06 mm, both p < 0.001).

CONCLUSION

An augmented reality platform employed on smartphone and smartglasses devices may provide accurate display and navigation guidance for percutaneous needle-based interventions.

Novel Models for Identification of the Ruptured Aneurysm in Patients with Subarachnoid Hemorrhage with Multiple Aneurysms

BACKGROUND AND PURPOSE

In patients with SAH with multiple intracranial aneurysms, often the hemorrhage pattern does not indicate the rupture source. Angiographic findings (intracranial aneurysm size and shape) could help but may not be reliable. Our purpose was to test whether existing parameters could identify the ruptured intracranial aneurysm in patients with multiple intracranial aneurysms and whether composite predictive models could improve the identification.

MATERIALS AND METHODS

We retrospectively collected angiographic and medical records of 93 patients with SAH with at least 2 intracranial aneurysms (total of 206 saccular intracranial aneurysms, 93 ruptured), in which the ruptured intracranial aneurysm was confirmed through surgery or definitive hemorrhage patterns. We calculated 13 morphologic and 10 hemodynamic parameters along with location and type (sidewall/bifurcation) and tested their ability to identify rupture in the 93 patients. To build predictive models, we randomly assigned 70 patients to training and 23 to holdout testing cohorts. Using a linear regression model with a customized cost function and 10-fold cross-validation, we trained 2 rupture identification models: RIM_C using all parameters and RIM_M excluding hemodynamics.

RESULTS

The 25 study parameters had vastly different positive predictive values (31%-87%) for identifying rupture, the highest being size ratio at 87%. RIM_C incorporated size ratio, undulation index, relative residence time, and type; RIM_M had only size ratio, undulation index, and type. During cross-validation, positive predictive values for size ratio, RIM_M, and RIM_C were 86% ± 4%, 90% ± 4%, and 93% ± 4%, respectively. In testing, size ratio and RIM_M had positive predictive values of 85%, while RIM_C had 92%.

CONCLUSIONS

Size ratio was the best individual factor for identifying the ruptured aneurysm; however, RIM_C, followed by RIM_M, outperformed existing parameters.

Computer-Assisted Three-Dimensional Morphology Evaluation of Intracranial Aneurysms

OBJECTIVE

Precise morphologic evaluation is important for intracranial aneurysm (IA) management. At present, clinicians manually measure the IA size and neck diameter on 2-dimensional (2D) digital subtraction angiographic (DSA) images and categorize the IA shape as regular or irregular on 3-dimensional (3D)-DSA images, which could result in inconsistency and bias. We investigated whether a computer-assisted 3D analytical approach could improve IA morphology assessment.

METHODS

Five neurointerventionists evaluated the size, neck diameter, and shape of 39 IAs using current and computer-assisted 3D approaches. In the computer-assisted 3D approach, the size, neck diameter, and undulation index (UI, a shape irregularity metric) were extracted using semiautomated reconstruction of aneurysm geometry using 3D-DSA, followed by IA neck identification and computerized geometry assessment.

RESULTS

The size and neck diameter measured using the manual 2D approach were smaller than computer-assisted 3D measurements by 2.01 mm (P < 0.001) and 1.85 mm (P < 0.001), respectively. Applying the definitions of small IAs (<7 mm) and narrow-necked IAs (<4 mm) from the reported data, interrater variation in manual 2D measurements resulted in inconsistent classification of the size of 14 IAs and the necks of 19 IAs. Visual inspection resulted in an inconsistent shape classification for 23 IAs among the raters. Greater consistency was achieved using the computer-assisted 3D approach for size (intraclass correlation coefficient [ICC], 1.00), neck measurements (ICC, 0.96), and shape quantification (UI; ICC, 0.94).

CONCLUSIONS

Computer-assisted 3D morphology analysis can improve accuracy and consistency in measurements compared with manual 2D measurements. It can also more reliably quantify shape irregularity using the UI. Future application of computer-assisted analysis tools could help clinicians standardize morphology evaluations, leading to more consistent IA evaluations.

Shared and Distinct Rupture Discriminants of Small and Large Intracranial Aneurysms

BACKGROUND AND PURPOSE

Many ruptured intracranial aneurysms (IAs) are small. Clinical presentations suggest that small and large IAs could have different phenotypes. It is unknown if small and large IAs have different characteristics that discriminate rupture.

METHODS

We analyzed morphological, hemodynamic, and clinical parameters of 413 retrospectively collected IAs (training cohort; 102 ruptured IAs). Hierarchal cluster analysis was performed to determine a size cutoff to dichotomize the IA population into small and large IAs. We applied multivariate logistic regression to build rupture discrimination models for small IAs, large IAs, and an aggregation of all IAs. We validated the ability of these 3 models to predict rupture status in a second, independently collected cohort of 129 IAs (testing cohort; 14 ruptured IAs).

RESULTS

Hierarchal cluster analysis in the training cohort confirmed that small and large IAs are best separated at 5 mm based on morphological and hemodynamic features (area under the curve=0.81). For small IAs (<5 mm), the resulting rupture discrimination model included undulation index, oscillatory shear index, previous subarachnoid hemorrhage, and absence of multiple IAs (area under the curve=0.84; 95% confidence interval, 0.78-0.88), whereas for large IAs (≥5 mm), the model included undulation index, low wall shear stress, previous subarachnoid hemorrhage, and IA location (area under the curve=0.87; 95% confidence interval, 0.82-0.93). The model for the aggregated training cohort retained all the parameters in the size-dichotomized models. Results in the testing cohort showed that the size-dichotomized rupture discrimination model had higher sensitivity (64% versus 29%) and accuracy (77% versus 74%), marginally higher area under the curve (0.75; 95% confidence interval, 0.61-0.88 versus 0.67; 95% confidence interval, 0.52-0.82), and similar specificity (78% versus 80%) compared with the aggregate-based model.

CONCLUSIONS

Small (<5 mm) and large (≥5 mm) IAs have different hemodynamic and clinical, but not morphological, rupture discriminants. Size-dichotomized rupture discrimination models performed better than the aggregate model.

Identification of vortex structures in a cohort of 204 intracranial aneurysms

An intracranial aneurysm (IA) is a cerebrovascular pathology that can lead to death or disability if ruptured. Abnormal wall shear stress (WSS) has been associated with IA growth and rupture, but little is known about the underlying flow physics related to rupture-prone IAs. Previous studies, based on analysis of a few aneurysms or partial views of three-dimensional vortex structures, suggest that rupture is associated with complex vortical flow inside IAs. To further elucidate the relevance of vortical flow in aneurysm pathophysiology, we studied 204 patient IAs (56 ruptured and 148 unruptured). Using objective quantities to identify three-dimensional vortex structures, we investigated the characteristics associated with aneurysm rupture and if these features correlate with previously proposed WSS and morphological characteristics indicative of IA rupture. Based on the Q-criterion definition of a vortex, we quantified the degree of the aneurysmal region occupied by vortex structures using the volume vortex fraction (vVF) and the surface vortex fraction (sVF). Computational fluid dynamics simulations showed that the sVF, but not the vVF, discriminated ruptured from unruptured aneurysms. Furthermore, we found that the near-wall vortex structures co-localized with regions of inflow jet breakdown, and significantly correlated to previously proposed haemodynamic and morphologic characteristics of ruptured IAs.

Rupture Resemblance Models May Correlate to Growth Rates of Intracranial Aneurysms: Preliminary Results

BACKGROUND

Treatment of intracranial aneurysms (IAs) is largely guided by IA size and growth. Preliminary investigations have found a relationship between clinical factors and growth; yet, the relationship between morphologic and hemodynamic risk prediction models in IA growth is unknown.

METHODS

We analyzed serial images of 5 growing and 6 stable IAs. Rupture resemblance scores (RRSs) were calculated from three-dimensional segmented images and computational fluid dynamics simulations. The morphologic (RRS_M), hemodynamic (RRS_H), and combination (RRS_C) scores leveraged IA size ratio, wall shear stress, and oscillatory shear index. Comparisons of RRS and morphologic and hemodynamic characteristics were made between growing and stable IAs at the baseline time point and between the baseline and follow-up time points of the growing IAs. In addition, we investigated the correlation of growth rate and RRS and the hemodynamics of growing and stable regions were compared.

RESULTS

Our results indicate that there is no statistical difference in IAs at the baseline time point; however, growing IAs tend to have a higher aspect ratio (P = 0.066), undulation index (P = 0.086), and RRS_C (P = 0.86). In addition, we found a significant correlation between growth rate and baseline RRS of all 3 models (RRS_M, r = 0.874, P < 0.001; RRS_H, r = 0.727, P = 0.011; RRS_C, r = 0.815, P = 0.002). We also found that growing IAs significantly increased in aspect ratio (P = 0.034), size ratio (P = 0.034), and RRS_M (P = 0.034). Our results show that stable and growing regions had statistically different wall shear stress and oscillatory shear index.

CONCLUSIONS

Based on this preliminary study, we conjecture that aneurysms that resemble ruptured IAs may grow faster.

Initial Clinical Experience with AView-A Clinical Computational Platform for Intracranial Aneurysm Morphology, Hemodynamics, and Treatment Management

BACKGROUND

The management of intracranial aneurysm (IA) is challenging. Clinicians often rely on varied and intuitively disparate ways of evaluating rupture risk that may only partially take into account complex hemodynamic and morphologic factors. We developed a prototype of a clinically oriented, streamlined, computational platform, AView, for rapid assessment of hemodynamics and morphometrics in clinical settings. To show the potential clinical utility of AView, we report our initial multicenter experience highlighting the possible advantages of morphologic and hemodynamic analysis of IAs.

METHODS

AView software was deployed across 8 medical centers (6 in the United States, 2 in Japan). Eight clinicians were trained and used the AView software between September 2012 and January 2013.

RESULTS

We present 12 illustrative cases that show the potential clinical utility of AView. For all, morphology and hemodynamics, flow visualization, and rupture resemblance score (a surrogate for rupture risk) were provided. In 3 cases, AView could confirm the clinicians' decision to treat; in 3 cases, it could suggest which aneurysms may be at greater risk among multiple aneurysms; in 5 cases, AView could provide additional information for use during treatment decisions for ambiguous situations. In one stent-assisted coiling case, flow visualization predicted that the intuitive choice for stent placement could have resulted in sacrifice of an anterior cerebral artery due to blockage by coils and led clinicians to reconsider treatment plans.

CONCLUSIONS

AView has the potential to confirm decisions to treat IAs, suggest which among multiple aneurysms to treat, and guide treatment decisions. Furthermore, the flow visualization it affords can inform aneurysm treatment planning and potentially avoid poor outcomes.

Differences in Morphologic and Hemodynamic Characteristics for "PHASES-Based" Intracranial Aneurysm Locations

BACKGROUND AND PURPOSE

Several recent prospective studies have found that unruptured intracranial aneurysms at various anatomic locations have different propensities for future rupture. This study aims to uncover the lack of understanding regarding rupture-prone characteristics, such as morphology and hemodynamic factors, associated with different intracranial aneurysm location.

MATERIALS AND METHODS

We investigated the characteristics of 311 unruptured aneurysms at our center. Based on the PHASES study, we separated and compared morphologic and hemodynamic characteristics among 3 aneurysm location groups: 1) internal carotid artery; 2) middle cerebral artery; and 3) anterior communicating, posterior communicating, and posterior circulation arteries.

RESULTS

A mixed model statistical analysis showed that size ratio, low wall shear stress area, and pressure loss coefficient were different between the intracranial aneurysm location groups. In addition, a pair-wise comparison showed that ICA aneurysms had lower size ratios, lower wall shear stress areas, and lower pressure loss coefficients compared with MCA aneurysms and compared with the group of anterior communicating, posterior communicating, and posterior circulation aneurysms. There were no statistical differences between MCA aneurysms and the group of anterior communicating, posterior communicating, and posterior circulation aneurysms for morphologic or hemodynamic characteristics.

CONCLUSIONS

ICA aneurysms may be subjected to less rupture-prone morphologic and hemodynamic characteristics compared with other locations, which could explain the decreased rupture propensity of intracranial aneurysms at this location.

Computer-Assisted Adjuncts for Aneurysmal Morphologic Assessment: Toward More Precise and Accurate Approaches

Neurosurgeons currently base most of their treatment decisions for intracranial aneurysms (IAs) on morphological measurements made manually from 2D angiographic images. These measurements tend to be inaccurate because 2D measurements cannot capture the complex geometry of IAs and because manual measurements are variable depending on the clinician's experience and opinion. Incorrect morphological measurements may lead to inappropriate treatment strategies. In order to improve the accuracy and consistency of morphological analysis of IAs, we have developed an image-based computational tool, AView. In this study, we quantified the accuracy of computer-assisted adjuncts of AView for aneurysmal morphologic assessment by performing measurement on spheres of known size and anatomical IA models. AView has an average morphological error of 0.56% in size and 2.1% in volume measurement. We also investigate the clinical utility of this tool on a retrospective clinical dataset and compare size and neck diameter measurement between 2D manual and 3D computer-assisted measurement. The average error was 22% and 30% in the manual measurement of size and aneurysm neck diameter, respectively. Inaccuracies due to manual measurements could therefore lead to wrong treatment decisions in 44% and inappropriate treatment strategies in 33% of the IAs. Furthermore, computer-assisted analysis of IAs improves the consistency in measurement among clinicians by 62% in size and 82% in neck diameter measurement. We conclude that AView dramatically improves accuracy for morphological analysis. These results illustrate the necessity of a computer-assisted approach for the morphological analysis of IAs.

Flow Instability Detected by High-Resolution Computational Fluid Dynamics in Fifty-Six Middle Cerebral Artery Aneurysms

Recent high-resolution computational fluid dynamics (CFD) studies have detected persistent flow instability in intracranial aneurysms (IAs) that was not observed in previous in silico studies. These flow fluctuations have shown incidental association with rupture in a small aneurysm dataset. The aims of this study are to explore the capabilities and limitations of a commercial cfd solver in capturing such velocity fluctuations, whether fluctuation kinetic energy (fKE) as a marker to quantify such instability could be a potential parameter to predict aneurysm rupture, and what geometric parameters might be associated with such fluctuations. First, we confirmed that the second-order discretization schemes and high spatial and temporal resolutions are required to capture these aneurysmal flow fluctuations. Next, we analyzed 56 patient-specific middle cerebral artery (MCA) aneurysms (12 ruptured) by transient, high-resolution CFD simulations with a cycle-averaged, constant inflow boundary condition. Finally, to explore the mechanism by which such flow instabilities might arise, we investigated correlations between fKE and several aneurysm geometrical parameters. Our results show that flow instabilities were present in 8 of 56 MCA aneurysms, all of which were unruptured bifurcation aneurysms. Statistical analysis revealed that fKE could not differentiate ruptured from unruptured aneurysms. Thus, our study does not lend support to these flow instabilities (based on a cycle-averaged constant inflow as opposed to peak velocity) being a marker for rupture. We found a positive correlation between fKE and aneurysm size as well as size ratio. This suggests that the intrinsic flow instability may be associated with the breakdown of an inflow jet penetrating the aneurysm space.

Challenges and limitations of patient-specific vascular phantom fabrication using 3D Polyjet printing

Additive manufacturing (3D printing) technology offers a great opportunity towards development of patient-specific vascular anatomic models, for medical device testing and physiological condition evaluation. However, the development process is not yet well established and there are various limitations depending on the printing materials, the technology and the printer resolution. Patient-specific neuro-vascular anatomy was acquired from computed tomography angiography and rotational digital subtraction angiography (DSA). The volumes were imported into a Vitrea 3D workstation (Vital Images Inc.) and the vascular lumen of various vessels and pathologies were segmented using a "marching cubes" algorithm. The results were exported as Stereo Lithographic (STL) files and were further processed by smoothing, trimming, and wall extrusion (to add a custom wall to the model). The models were printed using a Polyjet printer, Eden 260V (Objet-Stratasys). To verify the phantom geometry accuracy, the phantom was reimaged using rotational DSA, and the new data was compared with the initial patient data. The most challenging part of the phantom manufacturing was removal of support material. This aspect could be a serious hurdle in building very tortuous phantoms or small vessels. The accuracy of the printed models was very good: distance analysis showed average differences of 120 μm between the patient and the phantom reconstructed volume dimensions. Most errors were due to residual support material left in the lumen of the phantom. Despite the post-printing challenges experienced during the support cleaning, this technology could be a tremendous benefit to medical research such as in device development and testing.

Hemodynamic study of arteriovenous fistulas for hemodialysis access

Dialysis access failure and associated complications represent a major cause of morbidity in patients with renal failure. This is due to an incomplete understanding of the hemodynamics associated with both arteriovenous fistula (AVF) successes and complications. Several decades of research have been performed studying these complex hemodynamic changes. This review provides an overview of work undertaken in three key areas of AVF hemodynamic research: mathematical modeling, in vivo fluid dynamic measurements and in vitro fluid dynamic modeling. Current and future work is then summarized involving the application of a comprehensive, systematic study of dialysis access hemodynamics. The ultimate goal is the ability to predict clinical outcomes of dialysis access procedures through personalized, patient-specific surgical planning. If successful, this type of tool would allow surgeons to predict multiple-dialysis access intervention outcomes and choose a personalized approach to maximize success.

Abstract 424: Novel Computational Algorithm to Quantify Blood Flow and Vascular Resistance from Contrast Angiography with High Accuracy

OBJECTIVES: Contrast angiography can diagnose arterial occlusive disease but cannot provide hemodynamic data. Past work has attempted to calculate angiographic blood flow but clinical use has been limited by measurement error of >10%. Our hypothesis was that blood flow could be calculated from a contrast angiogram with <10% error through the application of a novel computational algorithm.

METHODS: A pulsatile, in vitro hemodynamic simulator with a light-based angiographic imaging system (InfiMed, Inc.) was used as the testing platform. Flow rates were varied through increases in outflow resistance and were directly measured with a Transonic flow meter (+/-4% error). An algorithm was designed to determine instantaneous flow from DICOM images using a combination of automatic vessel detection, segmentation, and time of flight bolus tracking. These calculated flow rates were compared to those directly measured.

RESULTS: The calculated flow rates (cc/min) were highly accurate when compared to those directly measured (4.1+/-3% error). Furthermore, time-density curves were accurate enough to detect relative changes in flow of 1.7 cc/sec reflecting changes in distal vascular resistance (Figure 1).

CONCLUSION: We conclude that using this approach, blood flow can be angiographically measured with increased accuracy relative to prior work. This may provide clinically reliable hemodynamic data to guide diagnostic and therapeutic interventions.